As demonstrated by other big data fields [1]}, such limitations can be reduced by developing parallel algorithms that combine the computational power of thousands of processing elements across distributed-memory clusters, and supercomputers. We, and others have developed high-performance computing (HPC) techniques for processing of MS data including for multicore [2]}, [3]}, [4]}, [5]}, and distributed-memory architectures [6]}, [7]} [8]}, [9]}, [10]}, [11]}. Similar to serial algorithms, the objective of these HPC methods has been to speed up the arithmetic scoring part of the search engines, by spawning multiple (managed) instances of the original code, replicating the theoretical database, and splitting the experimental data. However, computationally optimal HPC algorithms that minimize both the computational and communications costs for these tasks are still needed. Urgent need for developing methods that exhibit optimal performance is illustrated in our theoretical framework [12]}, and can potentially lead to large-scale systems biology studies especially for meta-proteomics, proteogenomic, and MS based microbiome or non-model organisms’ studies having direct impact on personalized nutrition, microbiome research, and cancer therapeutics.
